Logistic Regression -- An Overview with an Example

Known for its simplicity to understand, the Logistic Regression algorithm is very reliable and extremely useful, and that's why when it comes to binary classification problems, The Logistic Regression is any engineers go-to choice. The Logistic Regression uses the sigmoid function to output continuous probabilistic values between 0–1 for any value from its independent variables, and these probabilistic values are then compared against a threshold value of 0.5. Any value greater than 0.5 is classified in the "1 category class," and any value less than 0.5 is classified in the "0 category class" or the "class in which the particular event does not take place." A common question people ask is that, If Logistic Regression is used for classification problems, why does it have the "Regression" term in it? And why can't we use Linear Regression instead of Logistic Regression for classification problems? The answer to the first question is that Even though, The Logistic Regression is used for binary classification problems, The output from the sigmoid equation is still a continuous numerical value.